Can We Use Head-to-Head Market Movements in the Line Market?

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Yesterday's post led to an interesting Twitter thread last evening, which included a suggestion to reanalyse the data to determine whether price movements in the Pinnacle head-to-head market might have predictive value in other markets for the same game, specifically in the line market.

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Is There a Signal in Head-to-Head Market Movements?

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Watching the TAB markets as they've shifted across the course of a week I've often wondered if there might be something predictive in those movements. If the eventual favourite's price has shortened during the week, does it win more or less often than its closing price would suggest?

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When Favourites Lose: Accuracy or Scoring Shot Production?

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In today's post I'll review the performance of all the teams that have been assessed as favourites by the TAB in games played during the period 2006 to the end of Round 17 in 2015, excluding only those games where the TAB bookmaker installed equal-favourites.

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Why AFL Handicap-Adjusted Margins Are Normal : Part II

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In the previous blog on this topic I posited that the Scoring Shot production of a team could be modelled as a Poisson random variable with some predetermined mean, and that the conversion of these Scoring Shots into Goals could be modelled as a BetaBinomial with fixed conversion probability and theta (a spread parameter).

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Modelling Team Scores as Weibull Distributions : Part II

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In a previous post I discussed the possibility of modelling AFL team scores as Weibull distributions, finding that there was no compelling empirical or other reason to discount the idea and promising to conduct further analyses to more directly assess the Weibull distribution's suitability for the task.

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Do Favourites Kick Straighter Than Underdogs?

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We know that the TAB Bookmaker is exceptionally well-calibrated. Teams that he rates 80% chances win about 80% of the time and, more generally, teams that he rates X% chances win about X% of the time. Put another way, teams rated X% chances score more than their opponents X% of the time.

What about other scoring metrics, I wondered?

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Team Ratings, Bookmaker Prices and the Recent Predictability of Finals

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Last weekend saw three of four underdogs prevail in the first week of the Finals. Based on the data I have, you'd need to go back to 2006 to find a more surprising Week 1 of the Finals and, as highlighted in the previous blog, no matter how far you went back you wouldn't find a bigger upset than Port Adelaide's defeat of the Pies.
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Which Teams Fare Better as Favourites?

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In this blog, the next is a series in which I've been exploring the all-time MARS Ratings I created for every game from the start of 1897 to the end of the 2012 season, I'll be looking at how well each team has performed depending on the relative strength of its opponent, as measured by their MARS Rating. So, for example, we'll consider how well Collingwood tends to do when playing a team it is assessed as being much stronger than, a little stronger than, about as capable as, and so on.
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How Many Quarters Will the Favourite Win?

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Over the past few blogs I've been investigating the relationship between the result of each quarter of an AFL game and the pre-game head-to-head prices set for that same game. In the most recent blog I came up with an equation that allows us to estimate the probability that a team will win a quarter (p) using as input only that team's pre-game Implicit Victory Probability (V), which we can derive from the pre-game head-to-head prices as the ratio of the team's opponent's price divided by the sum of the two teams' prices.
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Does Crowd Size Affect Game Outcomes?

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Based on empirical evidence we know that there is a home ground advantage in AFL which, in part, might be attributable to the pro-Home team leanings amongst the majority of the crowd. In this blog I want to explore a slightly different question about the effects of the crowd: specifically, does the size of the crowd matter too?
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Estimating Fair Head-to-Head Prices : Part I

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You'll recall that the total overround embedded in the head-to-head market, ignoring the possibility of a draw, is calculated by summing the reciprocal of the head-to-head prices for each team. So, for example, if the head-to-head prices for a game were $1.20 / $4.60, the overround would be 1/1.2 + 1/4.6, which is 105.1%. Some subtract 1 from this figure and would report this overround as 5.1%.
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Projecting the Favourite's Final Margin

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In a couple of earlier blogs I created binary logit models to predict the probability that the favourite would win given a specified lead at a quarter break and the bookmaker's assessed pre-game probability for the favourite. These models allow you to determine what a fair in-running price would be for the favourite. You might instead want to know what the favourite's projected victory margin is given the same input data, so in this blog I'll be providing some simple linear regressions that provide this information.
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An Empirical Review of the Favourite In-Running Model

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In the previous blog we reviewed a series of binary logits that modelled a favourite's probability of victory given its pre-game bookmaker-assessed head-to-head probability and its lead at the end of a particular quarter. There I provided just a single indication of the quality of those models: the accuracy with which they correctly predicted the final result of the game. That's a crude and very broad measure. In this blog we'll take a closer look at the empirical model fits to investigate their performance in games with different leads and probabilities.
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Hanging Onto a Favourite: Assessing a Favourite's In-Running Chances of Victory

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Over the weekend I was paying particular attention to the in-running odds being offered on various games and remain convinced that punters overestimate the probability of the favourite ultimately winning, especially when the favourite trails.
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The Drivers of Overround

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What features of a contest, I wondered this week, led to it having a larger or smaller overround than an average game? In which games might the bookie be able to grab another quarter or half a percent, and in which might he be forced to round down the overround?
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A Friendly Wager on the Margin

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You're watching the footy with a mate who leans over and says he reckons the Cats will win by 15 points. How much leeway should you give him to make it a fair even money bet? Surprisingly - to me anyway - the answer is 24 points either way. So, if the Cats were to record any result between a loss by 9 points and a win by 39 points you should pay out.
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Why It Matters Which Team Wins

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In conversation - and in interrogation, come to think of it - the key to getting a good answer is often in the framing of the question.

So too in statistical modelling, where one common method for asking a slightly different question of the data is to take the variables you have and transform them.

Consider for example the following results for four binary logits, each built to provide an answer to the question 'Under what circumstances does the team with the higher MARS Rating tend to win?'.
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A Line Betting Enigma

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The TAB Sportsbet bookmaker is, as you know, a man to be revered and feared in equal measure. Historically, his head-to-head prices have been so exquisitely well-calibrated that I instinctively compare any model I construct with the forecasts he produces. To show that a model historically outperforms leads me to scuttle off to determine what error I've made in constructing the model, what piece of information I've used that, in truth, was only available with the benefit of hindsight.
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In-Running Wagering: What's the Best Strategy?

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With services such as Betfair now offering in-running wagering opportunities, the ability to accurately assess a team's chances of victory at any given point in a game is now of considerable commercial value. Imagine, for example, that your team, who are at home, lead by 18 points at the first change. Would a wager on them at $1.40 be advised?
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